What is being Measured in an Information Graphic?

  • Seniz Demir
  • Stephanie Elzer Schwartz
  • Richard Burns
  • Sandra Carberry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7816)


Information graphics (such as bar charts and line graphs) are widely used in popular media. The majority of such non-pictorial graphics have the purpose of communicating a high-level message which is often not repeated in the text of the article. Thus, information graphics together with the textual segments contribute to the overall purpose of an article and cannot be ignored. Unfortunately, information graphics often do not label the dependent axis with a full descriptor of what is being measured. In order to realize the high-level message of an information graphic in natural language, a referring expression for the dependent axis must be generated. This task is complex in that the required referring expression often must be constructed by extracting and melding pieces of information from the textual content of the graphic. Our heuristic-based solution to this problem has been shown to produce reasonable text for simple bar charts. This paper presents the extensibility of that approach to other kinds of graphics, in particular to grouped bar charts and line graphs. We discuss the set of component texts contained in these two kinds of graphics, how the methodology for simple bar charts can be extended to these kinds, and the evaluation of the enhanced approach.


Noun Phrase Component Text Line Graph Information Graphic Proper Noun 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Belz, A., Kow, E., Viethen, J., Gatt, A.: The grec challenge 2008: Overview and evaluation results. In: The Proceedings of the 5th International Natural Language Generation Conference (2008)Google Scholar
  2. 2.
    Burns, R., Carberry, S., Elzer, S., Chester, D.: Automatically Recognizing Intended Messages in Grouped Bar Charts. In: Cox, P., Plimmer, B., Rodgers, P. (eds.) Diagrams 2012. LNCS, vol. 7352, pp. 8–22. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Carberry, S., Elzer, S., Demir, S.: Information graphics: An untapped resource for digital libraries. In: The Proceedings of the ACM Special Interest Group on Information Retrieval Conference, pp. 581–588 (2006)Google Scholar
  4. 4.
    Clark, H.: Using Language. Cambridge University Press (1996)Google Scholar
  5. 5.
    Demir, S., Carberry, S., Elzer, S.: Issues in realizing the overall message of a bar chart. In: Recent Advances in Natural Language Processing, vol. 5, pp. 311–320. John Benjamins (2007)Google Scholar
  6. 6.
    Elzer, S., Carberry, S., Chester, D., Demir, S., Green, N., Zukerman, I., Trnka, K.: Exploring and exploiting the limited utility of captions in recognizing intention in information graphics. In: The Proceedings of the Annual Meeting on Association for Computational Linguistics, pp. 223–230 (2005)Google Scholar
  7. 7.
    Elzer, S., Carberry, S., Zukerman, I., Chester, D., Green, N., Demir, S.: A probabilistic framework for recognizing intention in information graphics. In: The Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1042–1047 (2005)Google Scholar
  8. 8.
    Fasciano, M., Lapalme, G.: Intentions in the coordinated generation of graphics and text from tabular data. Knowledge and Information Systems 2(3), 310–339 (2000)zbMATHCrossRefGoogle Scholar
  9. 9.
    Grosz, B., Sidner, C.: Attention, intentions, and the structure of discourse. Computational Linguistics 12(3), 175–204 (1986)Google Scholar
  10. 10.
    Kerpedjiev, S., Green, N., Moore, J., Roth, S.: Saying it in graphics: from intentions to visualizations. In: The Proceedings of the Symposium on Information Visualization, pp. 97–101 (1998)Google Scholar
  11. 11.
    Krahmer, E., van Erk, S., Verleg, A.: Graph-based generation of referring expressions. Computational Linguistics 29(1), 53–72 (2003)zbMATHCrossRefGoogle Scholar
  12. 12.
    Nenkova, A., McKeown, K.: References to named entities: a corpus study. In: The Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 70–72 (2003)Google Scholar
  13. 13.
    Wu, P., Carberry, S., Elzer, S., Chester, D.: Recognizing the Intended Message of Line Graphs. In: Goel, A.K., Jamnik, M., Narayanan, N.H. (eds.) Diagrams 2010. LNCS, vol. 6170, pp. 220–234. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Seniz Demir
    • 1
  • Stephanie Elzer Schwartz
    • 2
  • Richard Burns
    • 3
  • Sandra Carberry
    • 4
  2. 2.Dept. of Computer ScienceMillersville UniversityUSA
  3. 3.Dept. of Computer ScienceWest Chester UniversityUSA
  4. 4.Dept. of Computer and Information SciencesUniversity of DelawareUSA

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